A Review of a Text Classification Technique: K-Nearest Neighbor

DOI

10.2991/cisia-15.2015.123How to use a DOI?

Keywords

text clasificaton; rocchio-Knn; TW-kNN; RS-kNN; kNN based on K-Medoids

Abstract

In order to get effective information timely and accurately in masses of text, text classification techniques get extensive attention from many aspects. A lot of algorithms were proposed for text classification which made it easy to classify texts, such as Naïve Bayes, Rocchio, Decision Tree, Artificial Neural Networks, VSM, kNN and so on. In this paper, we mainly discussed the latest improved algorithm of kNN including Rocchio-kNN, TW-kNN, RS-kNN and kNN based on K-Medoids. Each of the representative algorithms is discussed in detail. These algorithms based on kNN have reduced the computational complexity as well as increased the execution efficiency compared with the traditional kNN algorithm.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - R.S Zhou
AU  - Z.J Wang
PY  - 2015/06
DA  - 2015/06
TI  - A Review of a Text Classification Technique: K-Nearest Neighbor
BT  - Proceedings of the International Conference on Computer Information Systems and Industrial Applications
PB  - Atlantis Press
SP  - 453
EP  - 455
SN  - 2352-538X
UR  - https://doi.org/10.2991/cisia-15.2015.123
DO  - 10.2991/cisia-15.2015.123
ID  - Zhou2015/06
ER  -